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Comparative analysis of transformer, CNN, and YOLO architectures for mandibular condyle segmentation on panoramic radiographs: a deep learning benchmark

Crossref DOI link: https://doi.org/10.1186/s12903-026-08228-3

Published Online: 2026-04-16

Update policy: https://doi.org/10.1007/springer_crossmark_policy

Authors

Yilmaz, Serkan https://orcid.org/0000-0001-7149-0324
Ozturk, Hilal Peker

Ozgedik, Hatice Seda

Avsever, Ismail Hakan

Senel, Bugra

Tasyurek, Murat

Kurt, Mehmet Hakan
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Text and Data Mining valid from 2026-04-16

Version of Record valid from 2026-04-16
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Article History

Received: 10 February 2026

Accepted: 24 March 2026

First Online: 16 April 2026

Declarations

:

: This retrospective deep learning study was performed in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards. The study protocol was formally reviewed and approved by the University of Health Sciences, Gülhane Scientific Research Ethics Committee (Approval ID: 2025/480).

: Not applicable.

: The authors declare no competing interests.

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